The purpose of this Document is to demonstrate gene-snp pairs that have high loadings. First we need to load the posterior wieghts and pis.

c=NULL
r=NULL
for(i in 1:nrow(post.weights)){
  pw.mat=matrix(as.matrix(post.weights[i,]),ncol=54,byrow=T)
  c[i]=which.max(colSums(pw.mat))
  r[i]=which.max(rowSums(pw.mat))
  }

write.table(c,"../Data/maxColsums.txt",row.names=rownames(z.stat))
write.table(r,"../Data/maxrowsums.txt",row.names=rownames(z.stat))  
})
for(i in 10:19){
weightplot(genename = which(cols==9&rows>10)[i],max.weight = 9,covmat = covmat)
}

for(i in 1:20){
print(c(i,rownames(z.stat)[which(cols==9&rows>10)[i]]))
}

First, we show examples with high loadings on \(U_K2\) and \(U_K3\) which are the rank 44 and rank 3 Output of ED, initialized with the Empirical Covariance Matrix and the Rank 3 dimensional reduction of it.

Next, we show examples from some of the single rank matrices. Note how in 8, testes is anticorrelated with everything.